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With technology growing by leaps and bounds, a colossal amount of data is being generated every minute. Big Data analytics has revolutionized the way businesses work by collecting, processing and analyzing this data to discover useful trends, patterns and insights that help them achieve a competitive edge and gain operational efficiency.
While analytics has been there for a long time, it is only recently that organisations have started using specialized tools like Hadoop and Apache for managing data.
Earlier, the same analysis was done using spreadsheets. Spreadsheets were time-consuming, used little data and, therefore, allowed only limited analysis.
As technology became ubiquitous, the amount of data generated increased in volume, variety and velocity rendering the traditional methods obsolete. Most of the businesses, be it manufacturing, healthcare, media, banking or telecommunication have now moved to Big Data solutions.
Big Data has helped them capture and manage the whopping amount of data generated from a broad range of channels like websites, mobile applications, social media, desktop, CRM, technological experiments, and—increasingly—sensors and other IoT devices.
1) Customer Acquisition and Customer Retention: One of the key application areas of Big Data analytics is the optimization of customer acquisition and customer retention. As reaching the prospects has become more convenient, the marketplace has become immensely competitive and customer acquisition and retention have become particularly difficult.
Big Data helps organisations to segment the customer data so that they can focus only on high-value prospects or customers. Organizations can identify the channels generating the maximum leads.
They can also study customer behaviour across all touch-points and thus map the entire customer journey and enact steps to make it more hassle-free.
Companies are employing Big Data solutions to study customers’ needs and preferences and pitch relevant products for up-selling and cross-selling.
A classic example in this regard is Amazon’s personalized recommendation engine. The engine records and analyses customer’s previous purchases, his searching history, products in his shopping cart, products in his wish list, products reviewed by him and uses this analysis to recommend additional products that other customers purchased when buying those items.
Big Data can unveil customer pain points to help companies incorporate suitable changes in their overall marketing strategy.
For instance, a company can analyze its social media pages and review mentions of its brand, customers’ reviews, complaints and tweak its offerings accordingly.
Read: How Big Data Analytics Optimizes your Online Marketing
2) Supply Change Management: Big Data provides powerful data processing capabilities which help optimize production, logistics and distribution networks.
Manufacturing companies use analytics in conjunction with IoT and machine learning to monitor and control the production process using high-tech sensors. Manufacturers are increasingly using predictive analytics to plan asset maintenance and prevent unscheduled downtime.
Logistic companies are using Big Data solutions to optimize delivery routes by integrating GPS data, weather data, traffic data, road maintenance data, personnel schedule and vehicle maintenance schedule into a system that can analyze past trends and advice accordingly.
The American supply chain giant UPS has, for instance, utilized analytics in a big way to streamline delivery. UPS discovered that their trucks turning left wasted a lot of time, fuel and led to traffic congestion as it obstructed the ongoing traffic.
This analysis alone helped them reduce the number of trucks, prevent fuel wastage and improve delivery by approximately 350,000 more packages every year. Their trucks now turn left only when absolutely necessary, not more than 10% of the time.
Delivery vehicles equipped with sensors have brought in more transparency in the delivery process. Companies can now track the vehicle and consignment in real-time to prevent pilferage of items and avoid delays by suggesting alternative routes as needed.
Big Data has also paved the way for better warehouse management. Warehouses have begun using sensors to collect reams of data on storage and movement of inventory.
Analysis of this data helps speed up the movement of inventory (using predictive modelling), improve warehouse safety, refine operational decision-making and bridge efficiency gaps.
In the coming times, Big Data is going to play a significant role in warehouse automation.
Organizations are striving to achieve a 100% automation of warehouses by deploying robotic package handling, sorting and automated forklifts. Predictive modelling can be used to schedule preventative maintenance of these machines and improve their life span.
3) Fraud Prevention: In today’s technology-driven world, information has become more accessible, and this has allowed criminals to come up with new and sophisticated methods of committing fraud. Big data offers solutions to detect fraud and, most times, prevent it using real-time tracking techniques.
Financial companies, for instance, can analyze millions of customer transactions to establish a baseline for normal customer activity. They can compare this baseline to real-time data to pinpoint anomalies that indicate potential fraud.
For example, if there have been two or more transactions from different cities using the same credit card within a single day, organisations should treat this as a red flag and notify the customer immediately.
They can also examine fraudulent transactions to draw valuable insights regarding the origin of fraudulent transactions, methods used to dupe customers and type of accounts targeted and warn their customers to be aware of such fraudsters.
Big data also helps pinpoint inconsistencies in the customer data which result from errors (and not fraud) so that they are not exploited by fraudsters.
For example, if fraudsters realize a particular customer has blocked his card, they might try to exploit this by calling the customer care masquerading as the customer and try to elicit sensitive information from customer care executive.
Insurance companies have deployed big data solutions to combat spurious claims. Several insurers analyze their internal data, such as call centre voice recordings, claimant’s social media feed and third party details on claimant’s salary, bills, credit history, criminal records, and address changes to identify potentially fraudulent claims.
For example, if an insurance company finds out that a particular claimant has a history of bankruptcy, they would become more circumspect in dealing with him.
Read: How Big Data and Machine Learning Shield BFSI from Financial Fraud
4) Human Resource Management: Companies are using analytics for improving all aspects of human resource management including recruitment, training and development, performance and compensation.
Companies are using Big Data to recruit the most suitable candidates by going beyond the conventional approach of scanning resumes.
Big Data solutions provide valuable insights on the data collected from a candidate’s social media feed including his skill set, his network, his interests and so on. Companies can thus identify the best prospects and reduce the time needed to fill in positions.
Analytics also helps companies ditch the established training methods and use online training techniques to bridge the gaps in skill and knowledge. Companies can now closely monitor the learning behaviour of an employee and identify his training needs.
Companies deploy performance management systems to record details of daily work by each employee. This information when analyzed over time provides valuable insights regarding his efficiency and productivity and can be an input for rewarding compensation. Companies can, thus, track employee performance closely and award compensation accordingly.
Recently, Bank of America started using HR analytics software that tracks body movements and interaction among employees and found out that the teams with maximum interaction were the most efficient. Such insights would not have been possible without Big Data.
Big Data can help employers adopt a more pro-active approach toward employee retention. People tend to stay longer at organizations offering appropriate rewards and benefits.
Through a proper feedback system in place, employers can collect and analyze the reasons behind employee satisfaction or dissatisfaction and incorporate necessary changes.
Big Data analytics can be a valuable investment for organizations that strive to achieve competitive advantage, reduce the cost of operations and improve customer retention.
Given the pace at which technology is growing, organizations already have ample data at their disposal. It is up to them to make the most by bringing appropriate Big Data systems in place.
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